DEEP LEARNING BASED SENTIMENT ANALYSIS OF STUDENTS AND TEACHERS FEEDBACK FOR ENHANCED EDUCATIONAL INSIGHT
DOI:
https://doi.org/10.29121/shodhkosh.v7.i12s.2026.8310Keywords:
Sentiment Analysis, Deep Learning, Student Feedback, Teacher Assessment, Perception Analysis, Natural Language Processing, Educational Data MiningAbstract [English]
The digital transformation of higher education has led to an unprecedented volume of textual feedback from both students and teachers. While traditional manual analysis of this data is increasingly impractical, current automated sentiment analysis tools often rely on basic machine learning models like Naive Bayes and Support Vector Machines (SVM) that struggle with the complex, context-dependent language found in educational settings. Existing research predominantly focuses on one-sided student evaluations of faculty performance, often limited to coarse-grained polarity classification (positive vs. negative). These approaches frequently fail to capture the underlying emotional states or specific instructional aspects that are crucial for meaningful institutional reform. This introduces a perception analysis framework that utilizes advanced Deep Learning architectures to enhance educational insights. Unlike previous tools that use surface-level vectorization like TF-IDF, this system employs contextualized word embedding and neural networks to achieve a more sophisticated understanding of semantic nuances. A key advancement of this tool is its bidirectional focus, analysing feedback from both students and teachers to provide a holistic, 360-degree view of the educational environment. The proposed tool will be evaluated against established benchmarks, moving beyond simple accuracy to include more robust metrics such as F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient (MCC). The analysis will transition from basic polarity to fine-grained, aspect-based sentiment and emotion detection, enabling more precise pinpointing of areas for improvement. By bridging the gap between coarse sentiment detection and deep semantic understanding, this research offers a more powerful diagnostic tool for educational administrators. The insights gained can support more effective planning and targeted intervention measures, ultimately enhancing teacher performance and student contentment in the evolving online and flexible learning landscape
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